1. Field of the Invention
The present invention relates to a method and apparatus for retrieving an image texture descriptor, and more particularly, to an image texture descriptor retrieving method for retrieving a texture descriptor which is used in searching and browsing an image and describes texture characteristics of the image, and an apparatus thereof.
2. Description of the Related Art
Recently, image texture has emerged as important visual features for searching and browsing a large set of similar image patterns. For example, a conventional texture descriptor for filtering a texture descriptor by a Gabor filter extracts a texture descriptor consisting of coefficients obtained by Gabor filtering. However, although conventional image texture descriptors consist of numerous vectors, it is quite difficult to visually perceive texture structures from the texture descriptor.
It is an object of the present invention to provide a method for retrieving an image texture descriptor which can perceptually capture the texture structures present in an image.
It is another object of the present invention to provide a computer readable storage medium having a computer program stored therein, the program being arranged such that a computer executes the image texture descriptor retrieving method.
It is still another object of the present invention to provide an image texture descriptor retrieving apparatus which performs the image texture descriptor retrieving method.
To achieve the above object, there is provided a method for retrieving an image texture descriptor for describing texture features of an image, including the steps of (a) filtering input images using predetermined filters having different orientation coefficients, (b) projecting the filtered images onto axes of each predetermined direction to obtain data groups consisting of averages of each directional pixel values, (c) selecting candidate data groups among the data groups by a predetermined classification method, (d) determining a plurality of indicators based on orientation coefficients of the filters used in filtering the candidate data groups, and (e) determining the plurality of indicators as the texture descriptor of the image.
The step (a) may further include the step of (a-1) filtering input images using predetermined filters having different scale coefficients, and the step (d) further comprises the step of (d-1) determining a plurality of indicators based on scale coefficients of the filters used in filtering the candidate data groups.
The image texture descriptor retrieving method may further include the step of determining another indicator based on the presence of data groups filtered by filters having scale coefficients or orientation coefficients which are close to or identical with the scale coefficients or orientation coefficients of the filters used in filtering the selected candidate data groups.
The image texture descriptor retrieving method may further include the step of calculating the mean and variance of pixels with respect to the filtered images, and obtaining a predetermined vector using the calculated mean and variance.
According to another aspect of the present invention, there is provided a method for retrieving an image texture descriptor for describing texture features of an image, including the steps of (a) filtering input images using predetermined filters having different scale coefficients, (b) projecting the filtered images onto axes of each predetermined direction to obtain data groups consisting of averages of each directional pixel values, (c) determining a plurality of indicators based on scale coefficients of the filters used in filtering data groups selected among the data groups by a predetermined selection method, (d) determining the plurality of indicators as the texture descriptor of the image.
According to still another aspect of the present invention, there is provided a method for retrieving an image texture descriptor for describing texture features of an image, comprising the steps of (a) filtering input images using predetermined filters having different orientation coefficients and different scale coefficients, (b) projecting the filtered images onto horizontal and vertical axes to obtain horizontal-axis projection graphs and vertical-axis projection graphs, (c) calculating normalized auto-correlation values for each graph, (d) obtaining a local maximums and a local minimum for each normalized auto-correlation value, at which the calculated normalized auto-correlation values form a local peak and a local valley at a predetermined section, (e) defining the average of the local maximums and the average the local minimums as contrast, (f) selecting graphs in which the ratio of the standard deviation to the average of the local maximums is less than or equal to a predetermined threshold as first candidate graphs, (g) determining the type of the second candidate graphs according to the number of graphs filtered by the filters having scale coefficients or orientation coefficients which are close to or identical with the scale coefficients or orientation coefficients of the filters used in filtering the selected second candidate graphs, (h) counting the numbers of graphs belonging to the respective types of second candidate graphs and determining predetermined weights of each type of second candidate graphs, (i) calculating the sum of products of the counted numbers of graphs and the determined weights to determine the calculation result value as a first indicator constituting a texture descriptor, (1) determining the orientation coefficients and scale coefficients of the second candidate graphs having the biggest contrast as second through fifth indicators, and (k) determining indicators including the first indicator and the second through fifth indicators as the texture descriptors of the corresponding image.
The image texture descriptor retrieving method may further include the step of calculating the mean and variance of pixels with respect to the filtered images, and obtaining a predetermined vector using the calculated mean and variance, wherein the step (k) includes the step of determining indicators including the first indicator, the second through fifth indicators and the predetermined vector as the texture descriptors of the corresponding image.
The normalized auto-correlation, denoted by NAC(k), is preferably calculated by the following formula:       NAC    ⁡          (      k      )        =                    ∑                  m          =          k                          N          -          1                    ⁢                        P          ⁡                      (                          m              -              k                        )                          ⁢                  P          ⁡                      (            m            )                                                        ∑                      m            =            k                                N            -            1                          ⁢                                            P              2                        ⁡                          (                              m                -                k                            )                                ⁢                                    ∑                              m                =                k                                            N                -                1                                      ⁢                                          P                2                            ⁡                              (                m                )                                                        
wherein N is a predetermined positive integer, an input image consists of Nxc3x97N pixels, a pixel position is represented by i, where i is a number from 1 to N, the projection graphs expressed by pixels of the pixel position i is represented by P(i) and k is a number from 1 to N.
The contrast is determined as:   contrast  =                    1        M            ⁢                        ∑                      i            =            1                    M                ⁢                  P_magn          ⁢                      (            i            )                                -                  1        L            ⁢                        ∑                      i            =            1                    L                ⁢                  V_magn          ⁢                      (            i            )                              
wherein P_magn (i) and V_magn (i) are the local maximums and local minimums determined in the step (d).
In the step (f), the graphs satisfying the following formula are selected as first candidate graphs:       S    d    ≤  α
wherein d and S are the average and standard deviation of the local maximums and xcex1 is a predetermined threshold.
The step (g) includes the sub-steps of (g-1), if there are one or more graphs having scale or orientation coefficients identical with those of a pertinent candidate graph and one or more graphs having scale or orientation coefficients close to those of the pertinent candidate graph, classifying the pertinent candidate graph as a first type graph, (g-2) if there are one or more graphs having scale or orientation coefficients identical with those of a pertinent candidate graph but there is no graph having scale or orientation coefficients close to those of the pertinent candidate graph, classifying the pertinent candidate graph as a second type graph, and (g-3) if there is no graph having scale or orientation coefficients identical with or close to those of a pertinent candidate graph, classifying the pertinent candidate graph as a third type graph.
The step (h) includes the step of counting the number of graphs belonging to each of the first through third types of graphs and determining predetermined weights for each of the types of graphs.
After the step of (f), there may be further included the step of applying a predetermined clustering algorithm to the first candidate graphs to select second candidate graphs.
The predetermined clustering algorithm is preferably modified agglomerative clustering.
Preferably, in the step (j), the orientation coefficient of a graph having the biggest contrast, among the horizontal-axis projection graphs, is determined as a second indicator; the orientation coefficient of a graph having the biggest contrast, among the vertical-axis projection graphs, is determined as a second indicator, the scale coefficient of a graph having the biggest contrast, among the horizontal-axis projection graphs, is determined as a fourth indicator; and the scale coefficient of a graph having the biggest contrast, among the vertical-axis projection graphs, is determined as a fifth indicator.
The step (j) may include the step of determining indicators including the first indicator, the second through fifth indicators and the predetermined vector as the texture descriptors of the corresponding image.
The predetermined filters preferably include Gabor filters.
To achieve the second object of the present invention, there is provided a computer readable medium having program codes executable by a computer to perform a method for an image texture descriptor for describing texture features of an image, the method including the steps of (a) filtering input images using predetermined filters having different orientation coefficients and different scale coefficients, (b) projecting the filtered images onto horizontal and vertical axes to obtain horizontal-axis projection graphs and vertical-axis projection graphs, (c) calculating normalized auto-correlation values for each graph, (d) obtaining a local maximums and a local minimums for each of normalized auto-correlation values, at which the calculated normalized auto-correlation value forms a local peak and a local valley at a predetermined section, (e) defining the average of the local maximums and the average the local minimums as contrast, (f) selecting graphs in which the ratio of the standard deviation to the average of the local maximums is less than or equal to a predetermined threshold as first candidate graphs, (g) determining the type of the second candidate graphs according to the number of graphs filtered by the filters having scale coefficients or orientation coefficients which are close to or identical with the scale coefficients or orientation coefficients of the filters used in filtering the selected second candidate graphs, (h) counting the numbers of graphs belonging to the respective types of second candidate graphs and determining predetermined weights of each type of second candidate graph, (i) calculating the sum of products of the counted numbers of graphs and the determined weights to determine the calculation result value as a first indicator constituting a texture descriptor, (j) determining the orientation coefficients and scale coefficients of the second candidate graphs having the biggest contrast as second through fifth indicators, and (k) determining indicators including the first indicator and the second through fifth indicators as the texture descriptors of the corresponding image.
To achieve the third object of the present invention, there is provided an apparatus for retrieving an image texture descriptor for describing texture features of an image, the apparatus including filtering means for filtering input images using predetermined filters having different orientation coefficients, projecting means for projecting the filtered images onto axes of each predetermined direction to obtain data groups consisting of averages of each of the directional pixel values, classifying means for selecting candidate data groups among the data groups by a predetermined classification method, first indicator determining means for determining another indicator based on the number of graphs filtered by filters having scale coefficients or orientation coefficients which are close to or identical with the scale coefficients or orientation coefficients of the filters used in filtering the selected candidate graph, and second indicator determining means for determining a plurality of indicators based on scale coefficients and orientation coefficients of the filters used in filtering the determined candidate graphs.
Alternatively, there is provided an apparatus for retrieving an image texture descriptor for describing texture features of an image, the apparatus including a filtering unit for filtering input images using predetermined filters having different orientation coefficients and different scale coefficients, an image mean/variance calculating unit for calculating the mean and variance of pixels with respect to each of the filtered images, and obtaining a predetermined vector using the calculated mean and variance, a projecting unit for projecting the filtered images onto horizontal and vertical axes to obtain horizontal-axis projection graphs and vertical-axis projection graphs, a calculating unit for calculating a normalized auto-correlation value for each graph, a peak detecting/analyzing unit for detecting local maximums and local minimums for each auto-correlation value, at which the calculated normalized auto-correlation values forms a local peak and a local valley at a predetermined section, a mean/variance calculating unit for calculating the average of the local maximums and the average of the local minimums, a first candidate graph selecting/storing unit for selecting the graphs satisfying the requirement that the ratio of the standard deviation to the average of the local maximums be less than or equal to a predetermined threshold, as first candidate graphs, a second candidate graph selecting/storing unit for applying a predetermined clustering algorithm to the first candidate graphs to select the same as second candidate graphs, a classifying unit for counting the number of graphs belonging to each of the respective types of the second candidate graphs, outputting data signals indicative of the number of graphs of each type, determining predetermined weights of the graphs belonging to the respective types and outputting data signals indicative of weights to be applied to each type, a first indicator determining unit for calculating the sum of the products of the data representing the number of graphs belonging to each type, and the data representing the weights to be applied to each type, determining and outputting the calculation result as a first indicator constituting a texture descriptor, a contrast calculating unit for calculating the contrast according to formula (2) using the averages output from the mean/variance calculating unit and outputting a signal indicating that the calculated contrast is biggest, a second candidate graph selecting/storing unit for outputting the candidate graphs having the biggest contrast among the second candidate graphs stored therein in response to the signal indicating that the calculated contrast is biggest, a second-to-fifth indicator determining unit for determining the orientation coefficient of a graph having the biggest contrast, among the horizontal-axis projection graphs; the orientation coefficient of a graph having the biggest contrast, among the vertical-axis projection graphs, as a second indicator; the scale coefficient of a graph having the biggest contrast, among the horizontal-axis projection graphs, as a fourth indicator; and the scale coefficient of a graph having the biggest contrast, among the vertical-axis projection graphs, as a fifth indicator, and a texture descriptor output unit for combining the first indicator, the second through fifth indicators and the predetermined vector and outputting the combination result as the texture descriptors of the corresponding image